To demonstrate the applicability of WaveMix-Lite for image classification, we used multiple types of publicly available (under MIT Licenses) datasets based on the number of images and image size. Small datasets of smaller image sizes included CIFAR-10, CIFAR-100 [1]}, EMNIST [2]}, Fashion MNIST [3]}, and SVHN [4]}. Small datasets of larger image sizes included STL-10 [5]}, Caltech-256 [6]} and Tiny ImageNet [7]}. For benchmarking, we could not load the whole ImageNet-1K in \(224\times 224\)  resolution due to the resource constraints of Google Colab Pro+®. Therefore, we used ImageNet-1K of lower image resolution \(64\times 64\) . We also used larger datasets with larger images sizes such as, Places-365 [8]} and iNaturalist2021-10k (iNAT mini) [9]}. We used Cityscapes [10]} dataset for semantic segmentation experiments and evaluated performance in the Cityscapes validation dataset.
